I had been using gemma-4-31B-it llm for cost/accuracy and it was super cheap, about $0.65/month. I had also downscaled the size and back tested on bird/no-bird images from my camera.
But, then I noticed that it was missing birds that were at the back of the pool and I needed to use higher resolution and better models and cost went up 10x. Now, it's to high for my liking and I need to do more model testing to get it down to a few dollars a month that seems reasonable. I can also reduce frequency, I'm doing every 2 mins during daylight hours.
My sense has been that accuracy that I need is only possible with the pretty advanced AI models and that OpenCV type approach wouldn't be good enough. Birds are pretty small in the image of the overall pool that I am working with and discerning them accurately is hard. I think it would be possible for other use cases and basically depends on the image source you are working with and the difficulty of detection within that image.
If you had, say, a camera on the inside of a birdhouse and the whole scene was either empty of taken up by a bird, then that would be easy for an on-device model.
I'm seeing some pretty cool projects for bird detection with YOLO online. They may do more than you need; you probably don't care about the kind of bird, right?
Maybe it would be worth it to spend a few tokens on an implementation, just to try!
But, then I noticed that it was missing birds that were at the back of the pool and I needed to use higher resolution and better models and cost went up 10x. Now, it's to high for my liking and I need to do more model testing to get it down to a few dollars a month that seems reasonable. I can also reduce frequency, I'm doing every 2 mins during daylight hours.